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download cw_texts_clean_bert.csv\n","!mkdir output\n","!mkdir /data\n","!mv cw_texts_clean_bert.csv /data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QvRTCsEPvVCY","executionInfo":{"status":"ok","timestamp":1709408403897,"user_tz":480,"elapsed":5910,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"1f488ea7-a5db-4456-f339-c12bcef18656"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1a1v06PvGaWmTCWvL33o2C133D90SRzMC\n","To: /content/cw_texts_clean_bert.csv\n","100% 59.5M/59.5M [00:00<00:00, 69.3MB/s]\n"]}]},{"cell_type":"code","source":["# the following lines (177 in total) are directly copied from Haffner et al. (2023), Political Analysis\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import Dataset\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","from sklearn.metrics import mean_absolute_error\n","import time\n","from transformers import TrainingArguments, Trainer\n","import torch\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import r2_score\n","from numpy.random import seed\n","from tqdm import tqdm\n","seed(1)\n","\n","start = time.time()\n","final = pd.read_csv('/data/cw_texts_clean_bert.csv')\n","\n","print(len(final))\n","final['fatalities_log'] = final['fatalities_log'].astype(float)\n","\n","train = final[final['year'] < 2021]\n","train.reset_index(inplace=True, drop=True)\n","test = final[final['year'] == 2021]\n","test.reset_index(inplace=True, drop=True)\n","\n","train['split_dummy'] = ''\n","train['year'] = train['year'].astype(int)\n","train['month'] = train['month'].astype(int)\n","\n","for i in tqdm(range(len(train))):\n","    if train['year'][i] == 2020 and train['month'][i] > 6:\n","        train['split_dummy'][i] = 1\n","    else:\n","        train['split_dummy'][i] = 0\n","\n","train['split_dummy'].value_counts()\n","\n","train1 = train[train['split_dummy'] == 0]\n","vali = train[train['split_dummy'] == 1]\n","\n","X_train = train1['final_text']\n","y_train = train1['fatalities_log']\n","X_vali = vali['final_text']\n","y_vali = vali['fatalities_log']\n","X_test = test['final_text']\n","y_test = test['fatalities_log']\n","X_train = pd.DataFrame(X_train)\n","X_test = pd.DataFrame(X_test)\n","X_vali = pd.DataFrame(X_vali)\n","y_vali = pd.DataFrame(y_vali)\n","y_train = pd.DataFrame(y_train)\n","y_test = pd.DataFrame(y_test)\n","\n","df_train = pd.concat([X_train, y_train], axis=1)\n","df_val = pd.concat([X_vali, y_vali], axis=1)\n","df_test = pd.concat([X_test, y_test], axis=1)\n","\n","\n","raw_train_ds = Dataset.from_pandas(df_train)\n","raw_val_ds = Dataset.from_pandas(df_val)\n","raw_test_ds = Dataset.from_pandas(df_test)\n","\n","BASE_MODEL = \"snowood1/ConfliBERT-scr-uncased\"\n","LEARNING_RATE = 2e-5 # decreased from 2e-3\n","MAX_LENGTH = 256\n","BATCH_SIZE = 16\n","EPOCHS = 10 # reduced from 20\n","\n","tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n","def model_init():\n","    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=1, ignore_mismatched_sizes=True)\n","    # for param in model.bert.parameters():\n","    #     param.requires_grad = False\n","    return model\n","\n","\n","ds = {\"train\": raw_train_ds, \"validation\": raw_val_ds, \"test\": raw_test_ds}\n","\n","\n","def preprocess_function(examples):\n","    label = examples[\"fatalities_log\"]\n","    examples = tokenizer(examples[\"final_text\"], truncation=True, padding=\"max_length\", max_length=256)\n","\n","    # Change this to real number\n","    examples[\"label\"] = float(label)\n","    return examples\n","\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","\n","def compute_metrics_for_regression(eval_pred):\n","    logits, labels = eval_pred\n","    labels = labels.reshape(-1, 1)\n","\n","    mse = mean_squared_error(labels, logits)\n","    mae = mean_absolute_error(labels, logits)\n","    r2 = r2_score(labels, logits)\n","    single_squared_errors = ((logits - labels).flatten() ** 2).tolist()\n","\n","    # Compute accuracy\n","    # Based on the fact that the rounded score = true score only if |single_squared_errors| < 0.5\n","    accuracy = sum([1 for e in single_squared_errors if e < 0.25]) / len(single_squared_errors)\n","\n","    return {\"mse\": mse, \"mae\": mae, \"r2\": r2, \"accuracy\": accuracy}\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/data/output/conflibert\",\n","    learning_rate=LEARNING_RATE,\n","    per_device_train_batch_size=BATCH_SIZE,\n","    per_device_eval_batch_size=BATCH_SIZE,\n","    num_train_epochs=EPOCHS,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=500,\n","    save_total_limit=10,\n","    load_best_model_at_end=True,\n","    weight_decay=0.01,\n","    seed=123\n",")\n","\n","class RegressionTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(**inputs)\n","        logits = outputs[0][:, 0]\n","        loss = torch.nn.functional.mse_loss(logits, labels)\n","        return (loss, outputs) if return_outputs else loss\n","\n","\n","trainer = RegressionTrainer(\n","    model_init=model_init,\n","    args=training_args,\n","    train_dataset=ds[\"train\"],\n","    eval_dataset=ds[\"validation\"],\n","    compute_metrics=compute_metrics_for_regression,\n",")\n","\n","trainer.train()\n","\n","for obj in trainer.state.log_history:\n","    print(obj)\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()\n","\n","raw_pred_test, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred_test[:20])\n","pd.DataFrame(raw_pred_test).to_csv('/data/cw_pred_test_zero.csv')\n","\n","df_results = pd.DataFrame(columns=['Test_MSE','Test_R2'], index=range(0,1))\n","mse = mean_squared_error(ds['test']['fatalities_log'], raw_pred_test)\n","r_squared = r2_score(ds['test']['fatalities_log'], raw_pred_test)\n","print(mse)\n","print(r_squared)\n","df_results['Test_MSE'][0] = mse\n","df_results['Test_R2'][0] = r_squared\n","df_results.to_csv('/data/bert_metrics.csv', index=False)\n","\n","## Whole CW\n","print('Now whole corpus')\n","raw_train1_ds = Dataset.from_pandas(final)\n","ds = {\"test\": raw_train1_ds}\n","\n","## Create dataset\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","print('Now predicting')\n","raw_pred, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred[:20])\n","pd.DataFrame(raw_pred).to_csv('/data/cw_bert_pred_zero.csv')\n","\n","print('DF saved')\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["462ab2213b40477789d1c37c91e75d7f","735618ad69634a118b138a79e3a8ebc1","0b228a84a5bf449a8e55f27f94b309a3","b7ccfb64f3f442bdaf5e92eca598028c","88369829bb5b4fc68e522853022560c2","0b25039ed5344646ab7efc3b47420faa","06b61fa1ad4d4c238ec000db32f662a4","fe0e8d5a03354f069d7e38aeccea917d","3416c08eba734438b029410f4c307e5f","2138281c30804f268746cc151a557204","571d915a018641ad8a2c9e03187b5188","499d67bd80d74a5591573c2a6f0395cf","e50546349ed8438ebf5afc0912e1f42a","b90855593dc84cffae0c2ba028f407f8","ec8740d101b444488d243415fd439fcc","c7f188d2028741ce94cae233ec6c48d1","679dd13c3c4a4568bc9c36b36d91ab7e","514db46b51ac49b4b057a0313fc31211","97bf92c8c8f04cffa10e677ce97b969d","d06d0e325b204715b23d07c0440e82a9","b1e643e474014b45888c6591fe0ccf46","ee23a99711b84b24b7992b76ded4baf0","616d4350048f471cbbfced669c06afc6","6913f393e6524f0588185ad22a36c3b2","57aec9df731142c79542c3d3fc7f78d7","37294ed1b70d401d81aa3cfb634a485e","70641d3024c14d38b3688b8944349cb4","fb584bf6e2d94dc78049c06799b806b2","98df94cc1de64bd3b84fec8e896a622d","f0932dc96746441185cf964c15d595cf","5100e1be7a724c738fec61cd0ed5d53c","02479be444bf497e9390c2ada1d02c82","c115b227d0ab4563990d3d2598879fd9","08abdd4c92f2440f89a95815ed774e51","75589766b5ff4f5ca594980e401e3d26","bc567fed46d441c48383b643d7695896","4e0889a92dce4be1ad127c6ca3b52307","e6869057f6cb4e33a356c7088ea43723","f3d61786b65147bb8002727c32437e0e","42a970e03ee14631ae0463d8d6f7e755","a449e0a7e3ed48c9a9c0925277264fbe","986a5c5195bb498094f9ac9629ff7954","3b522365e57e4d3c96ad53c23de41a78","0c464ba62f244356b949bcc8fd4e3cb0","c1ef0127dd314cbaa9ca7b00aee91089","f46872fa05a14523bdea2302cf8292b3","40f97f72cbac4ca6801bcefe350db724","590d84dff5f74bdf8af504546a97058f","4472290d201d432099f0ce1bed5a62a0","1bb261d734f94468b60a9620cbbcb21a","5759c198aa514346881c2788b91b7568","79cea8cd755248b3920f1de5684f6d20","be64b04ae8c445deb553cbf4bbc81812","0dd0f3ff8eef47ad8dad5206a1f2de1e","784c11cb84704724ad509890710dbf4c","c4eacb0d40f04fceb21f76cd9623c07f","7df931dc221e4b9eaa6a6aeaa124051e","316323af07144729bb5e61980b896b0b","ec58d6c0361045869a62a0e5017857c8","5cb7fe7d61024d2c9ae07290bf8306e2","ef8edad17b2c4bdd8aed1a78bf45e09d","9480097c42854c7683185ac7835beb33","b460db93d5c643798e0f9ff10a7f6faa","ec89d9659a53417a8be820d5577e3984","07d4864d2f704eacbeef317d16c6a537","e6fb7a4e8bf54493a0d83d01d8aae9db","fde2f0446d80442bae5801aeb5255d6f","852b88607c0c4c3d9bbdfc8e1441b436","77064320ec9c43a9be77e34b9052b2fe","72e1dbeb16514e90a3bef931b8dd4c9a","040406ff6b3e41fcaaece7a85b69b75f","28819040ed0443d8b29c5782fa6eb31f","c769d184512f4d88a2b290951019cfe5","ff6cc67de3b34a22844726fcbcc0ad98","107f547cf4f14da5953698429947f27a","4c6367e021944eca9f2b591569958537","2ea8396c1e244183af60b85b34ca0179","c01505dd5fe14a5a9fdaede3f8dd8469","58d88d32b9e3433fbc3fe995b5f271af","dfbafb1730ba4fafabd1dafd9448c42c","7ab97e879e574e5eadafd30aedd36574","80ac73c966ac4475afdfbc43e47c8297","be53cef775f142ec850e3347de7f2b02","5433b00e69ae43f895bc8f22098a9305","3730df61788243a3b8204b8eb669f3ab","7cba453908ce4331a19cbdb5ce25de03","1b07dc1245334a97b923c52b7b9a325f","89f1f98609b044318d7c1d8e82bb1977","be2fabfb83734c62b80db73d4ef37499","003858d02df54de580eb92109e176b93","c5b408671f4f4160a361541ccb29bff2","381787b3bf7f4072acb9c9ea5342d5f2","f5e690e2398142b3b56bab4f03e139f4","fdf905af35eb49b5a767c72538cd5016","c92faf7208494b6e861ba06b73723e7c","662313803ac54cb19c9ecdc94af73c96","1a099fc851f24e5a9cf75775f56528ae","bb1698b9d5ce42ac9a0ad40a0a20d4cd","88873141eaf74991b7f784f11965a01d","815d09b0b3b8492e905bd740c2fd4f33","189c30738ff846b6b60e655f3ac460d0","bf196bd1057249baac781ecf108af080","6a2a41830cca4a099a4dafa179486c6c","9854b9d0c6de4216b2a8e5129dd41430","8adeaa1cf8fd4f928f456adf82f387c7","5b2676981d0e49dc9d2122233968cbe5","ed12c2a1e1ae484d8f0512316d192b85","e0a6a1a790d34562b6863a459471d724","b68b6d0b2f72444c8c594e2978ac7e51","f11a7d5e3b6b4000a67471274f38abd4"]},"id":"_UUtlbRAySQ_","outputId":"a944e97f-30b1-4b74-f036-344b7342bbd0","executionInfo":{"status":"ok","timestamp":1709411374556,"user_tz":480,"elapsed":2970667,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}}},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-3-f796d6d3b817>:28: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'] = ''\n","<ipython-input-3-f796d6d3b817>:29: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['year'] = train['year'].astype(int)\n","<ipython-input-3-f796d6d3b817>:30: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['month'] = train['month'].astype(int)\n"]},{"output_type":"stream","name":"stdout","text":["23868\n"]},{"output_type":"stream","name":"stderr","text":["  0%|          | 0/22572 [00:00<?, ?it/s]<ipython-input-3-f796d6d3b817>:36: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 0\n","<ipython-input-3-f796d6d3b817>:34: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 1\n","100%|██████████| 22572/22572 [00:06<00:00, 3227.11it/s]\n","/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"462ab2213b40477789d1c37c91e75d7f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"499d67bd80d74a5591573c2a6f0395cf"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|          | 0.00/224k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"616d4350048f471cbbfced669c06afc6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/454k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"08abdd4c92f2440f89a95815ed774e51"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|     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examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c01505dd5fe14a5a9fdaede3f8dd8469"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/437M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"be2fabfb83734c62b80db73d4ef37499"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='13710' max='13710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [13710/13710 45:08, Epoch 10/10]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Mse</th>\n","      <th>Mae</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>1.336200</td>\n","      <td>1.269518</td>\n","      <td>1.269518</td>\n","      <td>0.652294</td>\n","      <td>0.675895</td>\n","      <td>0.665123</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.908100</td>\n","      <td>1.210866</td>\n","      <td>1.210866</td>\n","      <td>0.657599</td>\n","      <td>0.690868</td>\n","      <td>0.683642</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.832400</td>\n","      <td>1.190945</td>\n","      <td>1.190945</td>\n","      <td>0.572844</td>\n","      <td>0.695954</td>\n","      <td>0.700617</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.647700</td>\n","      <td>1.193570</td>\n","      <td>1.193570</td>\n","      <td>0.636724</td>\n","      <td>0.695284</td>\n","      <td>0.697531</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>0.667900</td>\n","      <td>0.974331</td>\n","      <td>0.974331</td>\n","      <td>0.520100</td>\n","      <td>0.751255</td>\n","      <td>0.702160</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.636700</td>\n","      <td>1.150203</td>\n","      <td>1.150203</td>\n","      <td>0.574114</td>\n","      <td>0.706356</td>\n","      <td>0.674383</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>0.576500</td>\n","      <td>1.087575</td>\n","      <td>1.087576</td>\n","      <td>0.547557</td>\n","      <td>0.722344</td>\n","      <td>0.716049</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>0.522300</td>\n","      <td>1.006783</td>\n","      <td>1.006783</td>\n","      <td>0.518149</td>\n","      <td>0.742970</td>\n","      <td>0.722222</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>0.430000</td>\n","      <td>1.101234</td>\n","      <td>1.101234</td>\n","      <td>0.629263</td>\n","      <td>0.718857</td>\n","      <td>0.694444</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.473000</td>\n","      <td>0.968002</td>\n","      <td>0.968002</td>\n","      <td>0.492037</td>\n","      <td>0.752871</td>\n","      <td>0.717593</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>0.471000</td>\n","      <td>1.017858</td>\n","      <td>1.017858</td>\n","      <td>0.506631</td>\n","      <td>0.740143</td>\n","      <td>0.712963</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>0.390700</td>\n","      <td>0.948363</td>\n","      <td>0.948363</td>\n","      <td>0.466421</td>\n","      <td>0.757885</td>\n","      <td>0.731481</td>\n","    </tr>\n","    <tr>\n","      <td>6500</td>\n","      <td>0.383400</td>\n","      <td>0.935956</td>\n","      <td>0.935956</td>\n","      <td>0.484169</td>\n","      <td>0.761052</td>\n","      <td>0.729938</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>0.396200</td>\n","      <td>0.991413</td>\n","      <td>0.991413</td>\n","      <td>0.550371</td>\n","      <td>0.746894</td>\n","      <td>0.720679</td>\n","    </tr>\n","    <tr>\n","      <td>7500</td>\n","      <td>0.339700</td>\n","      <td>0.902015</td>\n","      <td>0.902015</td>\n","      <td>0.476094</td>\n","      <td>0.769717</td>\n","      <td>0.726852</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>0.338800</td>\n","      <td>0.881361</td>\n","      <td>0.881361</td>\n","      <td>0.472648</td>\n","      <td>0.774990</td>\n","      <td>0.740741</td>\n","    </tr>\n","    <tr>\n","      <td>8500</td>\n","      <td>0.329400</td>\n","      <td>0.952824</td>\n","      <td>0.952824</td>\n","      <td>0.493717</td>\n","      <td>0.756746</td>\n","      <td>0.711420</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>0.310500</td>\n","      <td>0.898989</td>\n","      <td>0.898989</td>\n","      <td>0.478277</td>\n","      <td>0.770490</td>\n","      <td>0.722222</td>\n","    </tr>\n","    <tr>\n","      <td>9500</td>\n","      <td>0.300900</td>\n","      <td>1.023498</td>\n","      <td>1.023498</td>\n","      <td>0.554612</td>\n","      <td>0.738703</td>\n","      <td>0.725309</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>0.295100</td>\n","      <td>0.959252</td>\n","      <td>0.959252</td>\n","      <td>0.480423</td>\n","      <td>0.755105</td>\n","      <td>0.728395</td>\n","    </tr>\n","    <tr>\n","      <td>10500</td>\n","      <td>0.272900</td>\n","      <td>0.903760</td>\n","      <td>0.903760</td>\n","      <td>0.467351</td>\n","      <td>0.769272</td>\n","      <td>0.734568</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>0.283900</td>\n","      <td>0.916440</td>\n","      <td>0.916440</td>\n","      <td>0.475964</td>\n","      <td>0.766035</td>\n","      <td>0.731481</td>\n","    </tr>\n","    <tr>\n","      <td>11500</td>\n","      <td>0.275600</td>\n","      <td>0.924905</td>\n","      <td>0.924905</td>\n","      <td>0.469481</td>\n","      <td>0.763874</td>\n","      <td>0.737654</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>0.269200</td>\n","      <td>0.902226</td>\n","      <td>0.902226</td>\n","      <td>0.468274</td>\n","      <td>0.769664</td>\n","      <td>0.731481</td>\n","    </tr>\n","    <tr>\n","      <td>12500</td>\n","      <td>0.261000</td>\n","      <td>0.904358</td>\n","      <td>0.904358</td>\n","      <td>0.459434</td>\n","      <td>0.769119</td>\n","      <td>0.722222</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>0.240400</td>\n","      <td>0.902572</td>\n","      <td>0.902572</td>\n","      <td>0.464854</td>\n","      <td>0.769575</td>\n","      <td>0.736111</td>\n","    </tr>\n","    <tr>\n","      <td>13500</td>\n","      <td>0.273000</td>\n","      <td>0.904184</td>\n","      <td>0.904184</td>\n","      <td>0.463870</td>\n","      <td>0.769164</td>\n","      <td>0.728395</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'loss': 1.3362, 'learning_rate': 1.927060539752006e-05, 'epoch': 0.36, 'step': 500}\n","{'eval_loss': 1.2695175409317017, 'eval_mse': 1.2695176601409912, 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'eval_r2': 0.7691636416403971, 'eval_accuracy': 0.7283950617283951, 'eval_runtime': 2.5398, 'eval_samples_per_second': 255.143, 'eval_steps_per_second': 16.143, 'epoch': 9.85, 'step': 13500}\n","{'train_runtime': 2710.46, 'train_samples_per_second': 80.887, 'train_steps_per_second': 5.058, 'total_flos': 2.884197492578304e+16, 'train_loss': 0.45872610636126115, 'epoch': 10.0, 'step': 13710}\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[[ 7.4548025 ]\n"," [ 6.8220134 ]\n"," [ 7.029536  ]\n"," [ 7.473029  ]\n"," [ 7.8078804 ]\n"," [ 7.5583286 ]\n"," [ 7.579903  ]\n"," [ 7.6177373 ]\n"," [ 7.5291123 ]\n"," [ 7.3145404 ]\n"," [ 7.044884  ]\n"," [ 6.51368   ]\n"," [-0.04706215]\n"," [ 1.2483698 ]\n"," [-0.04706215]\n"," [-0.04706215]\n"," [-0.04706215]\n"," [-0.04706215]\n"," [-0.04706215]\n"," [-0.04706215]]\n","0.9854993514887238\n","0.7749929830474538\n","Now whole corpus\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/23868 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"815d09b0b3b8492e905bd740c2fd4f33"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Now predicting\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[[4.757804 ]\n"," [4.1733603]\n"," [5.1161385]\n"," [3.5681956]\n"," [4.2316957]\n"," [4.9236035]\n"," [3.2647293]\n"," [4.6793246]\n"," [3.9463239]\n"," [4.4774036]\n"," [4.8604527]\n"," [3.7892141]\n"," [5.0081615]\n"," [5.2035556]\n"," [4.233014 ]\n"," [3.469781 ]\n"," [3.6064544]\n"," [3.0475998]\n"," [3.394722 ]\n"," [3.8247967]]\n","DF saved\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1492' max='1492' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1492/1492 01:32]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 0.337476521730423,\n"," 'eval_mse': 0.337476521730423,\n"," 'eval_mae': 0.24772536754608154,\n"," 'eval_r2': 0.9039952480987055,\n"," 'eval_accuracy': 0.8864588570470924,\n"," 'eval_runtime': 92.8514,\n"," 'eval_samples_per_second': 257.056,\n"," 'eval_steps_per_second': 16.069,\n"," 'epoch': 10.0}"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["end = time.time()\n","print(f\"The program took {(end - start) // 60} minutes in total.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8Vs9MicprTih","executionInfo":{"status":"ok","timestamp":1709411374557,"user_tz":480,"elapsed":18,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"fdf37304-628d-43be-f273-3cfa0c6d847e"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["The program took 49.0 minutes in total.\n"]}]}]}